Raw Data :

df.head()

	sender_name	timestamp_ms	content	type	photos	gifs	audio_files	year	month	hour
0	Mélanie Amazo'Night Bergeot	2020-03-29 07:55:58.952	:p	Generic	NaN	NaN	NaN	2020	3	07:55
1	Nicolas Cailleux	2020-03-29 07:43:47.446	t'as de quoi faire un peu quand meme :p	Generic	NaN	NaN	NaN	2020	3	07:43
2	Nicolas Cailleux	2020-03-29 07:43:41.209	exactement ça va	Generic	NaN	NaN	NaN	2020	3	07:43
3	Nicolas Cailleux	2020-03-29 07:43:38.139	enfin 59340	Generic	NaN	NaN	NaN	2020	3	07:43
4	Nicolas Cailleux	2020-03-29 07:42:49.732	60k msg	Generic	NaN	NaN	NaN	2020	3	07:42

The power of Data :

print(str(nb_day_first_last)+ ' day between ' +str(first_day)+ ' and ' +str(last_day))

1263 day between 2016-10-13 and 2020-03-29

#Calculate the number of "active day"
test_activeday=df
test_activeday['date'] = pd.to_datetime(test_activeday['timestamp_ms']).dt.date
nb_active_days=test_activeday["date"].nunique()
#%percentage of active day 
pc_active_days=int(((nb_active_days*100)/nb_day_first_last))
print("%d (%d percent) of those were ‘active’ days (i.e. messages were sent)."%(nb_active_days,pc_active_days))
#voir comment le modifier en pourcentage

1084 (85 percent) of those were ‘active’ days (i.e. messages were sent).

print("%d messages. I sent %d more messages than my boyfriend"%(nb_msg_send,nb_diff_msg_send))

59389 messages. I sent 1277 more messages than my boyfriend

print("%d unique words were used (many of which are not real words). I used %d unique words where my boyfriend used %d."%(nb_unique_word,nb_unique_word_mel,nb_unique_word_nico))

6927 unique words were used (many of which are not real words). I used 4244 unique words where my boyfriend used 2683.

print("2 participants - my boyfriends and I - living in FR most of the time and in our early twenties.")

2 participants - my boyfriends and I - living in FR most of the time and in our early twenties.

Message volume during our relationship

Wednesday i'm in love

Time after time

More than word

Once Loop Reflect